




'''simple docstring'''

from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class 		SCREAMING_SNAKE_CASE__  (			__A	):





			def 							A (      self						:    Optional[int]      ,		a_						:    Tuple							):



						"""simple docstring"""




						return 0.0







def __UpperCAmelCase	(   _UpperCAmelCase		:   np.ndarray		,				_UpperCAmelCase		:   int						)      ->      Dict:
			__snake_case			=		min([-20, np.min(fft_results[1 : samplerate // 2 - 1]						)]						)
			__snake_case			=		max([20, np.max(fft_results[1 : samplerate // 2 - 1]						)]						)
			return lowest, highest


def __UpperCAmelCase	(   _UpperCAmelCase		:   FilterType		,				_UpperCAmelCase		:   int						)      ->      int:
			__snake_case			=		5_12
			__snake_case			=		[1] + [0] * (size - 1)
			__snake_case			=		[filter_type.process(lowercase_						) for item in inputs]

			__snake_case			=		[0] * (samplerate - size)  # zero-padding
			outputs += filler
			__snake_case			=		np.abs(np.fft.fft(lowercase_						)						)
			__snake_case			=		20 * np.logaa(lowercase_						)

			# Frequencies on log scale from 24 to nyquist frequency
			plt.xlim(24		,				samplerate / 2 - 1						)
			plt.xlabel("Frequency (Hz)"						)
			plt.xscale("log"						)

			# Display within reasonable bounds
			__snake_case			=		get_bounds(lowercase_		,				lowercase_						)
			plt.ylim(max([-80, bounds[0]]						)		,				min([80, bounds[1]]						)						)
			plt.ylabel("Gain (dB)"						)

			plt.plot(lowercase_						)
			plt.show()






def __UpperCAmelCase	(   _UpperCAmelCase		:   FilterType		,				_UpperCAmelCase		:   int						)      ->      Tuple:
			__snake_case			=		5_12
			__snake_case			=		[1] + [0] * (size - 1)
			__snake_case			=		[filter_type.process(lowercase_						) for item in inputs]

			__snake_case			=		[0] * (samplerate - size)  # zero-padding
			outputs += filler
			__snake_case			=		np.angle(np.fft.fft(lowercase_						)						)

			# Frequencies on log scale from 24 to nyquist frequency
			plt.xlim(24		,				samplerate / 2 - 1						)
			plt.xlabel("Frequency (Hz)"						)
			plt.xscale("log"						)

			plt.ylim(-2 * pi		,				2 * pi						)
			plt.ylabel("Phase shift (Radians)"						)
			plt.plot(np.unwrap(lowercase_		,				-2 * pi						)						)
			plt.show()













'''simple docstring'''







import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
__lowerCAmelCase        =							logging.getLogger(__name__)


def       __UpperCamelCase					(      lowercase_       :  str   ):
       """simple docstring"""




       a_						     =			git.Repo(search_parent_directories=lowercase_   )
       a_						     =			{
           'repo_id': str(lowercase_   ),
           'repo_sha': str(repo.head.object.hexsha   ),
           'repo_branch': str(repo.active_branch   ),
       }

       with open(os.path.join(lowercase_   ,       'git_log.json'   )   ,       'w'   ) as f:
              json.dump(lowercase_   ,       lowercase_   ,       indent=4   )


def       __UpperCamelCase					(      lowercase_       :  List[Any]   ):
       """simple docstring"""




       if params.n_gpu <= 0:
              a_						     =			0
              a_						     =			-1
              a_						     =			True
              a_						     =			False
              return

       assert torch.cuda.is_available()

       logger.info('Initializing GPUs'   )
       if params.n_gpu > 1:
              assert params.local_rank != -1

              a_						     =			int(os.environ['WORLD_SIZE']   )
              a_						     =			int(os.environ['N_GPU_NODE']   )
              a_						     =			int(os.environ['RANK']   )

              # number of nodes / node ID
              a_						     =			params.world_size // params.n_gpu_per_node
              a_						     =			params.global_rank // params.n_gpu_per_node
              a_						     =			True

              assert params.n_nodes == int(os.environ['N_NODES']   )
              assert params.node_id == int(os.environ['NODE_RANK']   )

       # local job (single GPU)
       else:
              assert params.local_rank == -1

              a_						     =			1
              a_						     =			0
              a_						     =			0
              a_						     =			0
              a_						     =			1
              a_						     =			1
              a_						     =			False

       # sanity checks
       assert params.n_nodes >= 1
       assert 0 <= params.node_id < params.n_nodes
       assert 0 <= params.local_rank <= params.global_rank < params.world_size
       assert params.world_size == params.n_nodes * params.n_gpu_per_node

       # define whether this is the master process / if we are in multi-node distributed mode
       a_						     =			params.node_id == 0 and params.local_rank == 0
       a_						     =			params.n_nodes > 1

       # summary
       a_						     =			F'--- Global rank: {params.global_rank} - '
       logger.info(PREFIX + 'Number of nodes: %i' % params.n_nodes   )
       logger.info(PREFIX + 'Node ID        : %i' % params.node_id   )
       logger.info(PREFIX + 'Local rank     : %i' % params.local_rank   )
       logger.info(PREFIX + 'World size     : %i' % params.world_size   )
       logger.info(PREFIX + 'GPUs per node  : %i' % params.n_gpu_per_node   )
       logger.info(PREFIX + 'Master         : %s' % str(params.is_master   )   )
       logger.info(PREFIX + 'Multi-node     : %s' % str(params.multi_node   )   )
       logger.info(PREFIX + 'Multi-GPU      : %s' % str(params.multi_gpu   )   )
       logger.info(PREFIX + 'Hostname       : %s' % socket.gethostname()   )

       # set GPU device
       torch.cuda.set_device(params.local_rank   )

       # initialize multi-GPU
       if params.multi_gpu:
              logger.info('Initializing PyTorch distributed'   )
              torch.distributed.init_process_group(
                  init_method='env://'   ,       backend='nccl'   ,       )


def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       np.random.seed(args.seed   )
       torch.manual_seed(args.seed   )
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed   )




